scholarly journals Exploiting High Geopositioning Accuracy of SAR Data to Obtain Accurate Geometric Orientation of Optical Satellite Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3535
Author(s):  
Zhongli Fan ◽  
Li Zhang ◽  
Yuxuan Liu ◽  
Qingdong Wang ◽  
Sisi Zlatanova
Keyword(s):  
Satellite Imagery ◽  
Image Matching ◽  
Satellite Images ◽  
Three Dimensional ◽  
Phase Correlation ◽  
Feature Descriptor ◽  
Matching Method ◽  
Reference Images ◽  
Sar Data ◽  
Optical Satellite Images

Accurate geopositioning of optical satellite imagery is a fundamental step for many photogrammetric applications. Considering the imaging principle and data processing manner, SAR satellites can achieve high geopositioning accuracy. Therefore, SAR data can be a reliable source for providing control information in the orientation of optical satellite images. This paper proposes a practical solution for an accurate orientation of optical satellite images using SAR reference images to take advantage of the merits of SAR data. Firstly, we propose an accurate and robust multimodal image matching method to match the SAR and optical satellite images. This approach includes the development of a new structural-based multimodal applicable feature descriptor that employs angle-weighted oriented gradients (AWOGs) and the utilization of a three-dimensional phase correlation similarity measure. Secondly, we put forward a general optical satellite imagery orientation framework based on multiple SAR reference images, which uses the matches of the SAR and optical satellite images as virtual control points. A large number of experiments not only demonstrate the superiority of the proposed matching method compared to the state-of-the-art methods but also prove the effectiveness of the proposed orientation framework. In particular, the matching performance is improved by about 17% compared with the latest multimodal image matching method, namely, CFOG, and the geopositioning accuracy of optical satellite images is improved, from more than 200 to around 8 m.

scholarly journals Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

2019 ◽  
Vol 9 (2) ◽  
pp. 309-314
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Local Features ◽  
Feature Descriptor ◽  
Matching Method ◽  
Multiple Images ◽  
Feature Sets ◽  
Precise Relationship

Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.

The three-dimensional kinematics analysis of the shoulder joint using the image matching method with image correlation

2016 ◽  
Vol 2016 (0) ◽  
pp. S0220204
Author(s):  
健 下戸 ◽  
Takeshi SHIMOTO ◽  
Yoshitaka SHIRAISHI ◽  
Yifeng WANG ◽  
Satoru IKEBE ◽  
...  
Keyword(s):  
Shoulder Joint ◽  
Image Matching ◽  
Three Dimensional ◽  
Image Correlation ◽  
Kinematics Analysis ◽  
Matching Method

Research on image matching method of big data image of three-dimensional reconstruction

2015 ◽  
Author(s):  
Chunsen Zhang ◽  
Zhenguo Qiu ◽  
Shihuan Zhu ◽  
Xiqi Wang ◽  
Xiaolei Xu ◽  
...  
Keyword(s):  
Big Data ◽  
Image Matching ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Matching Method ◽  
Dimensional Reconstruction

scholarly journals Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

2017 ◽  
Vol 9 (6) ◽  
pp. 586 ◽  
Author(s):  
Nina Merkle ◽  
Wenjie Luo ◽  
Stefan Auer ◽  
Rupert Müller ◽  
Raquel Urtasun
Keyword(s):  
Satellite Images ◽  
Accuracy Improvement ◽  
Localization Accuracy ◽  
Sar Data ◽  
Optical Satellite Images

A combined image matching method for Chinese optical satellite imagery

2016 ◽  
Vol 9 (9) ◽  
pp. 851-872 ◽  
Author(s):  
Yansong Duan ◽  
Xu Huang ◽  
Jinxing Xiong ◽  
Yongjun Zhang ◽  
Bo Wang
Keyword(s):  
Satellite Imagery ◽  
Image Matching ◽  
Matching Method

scholarly journals ROBUST REGISTRATION FOR OPTICAL AND SAR IMAGES BASED ON SPATIAL GEOMETRIC CONSTRAINT AND STRUCTURE FEATURES

2020 ◽  
Vol V-1-2020 ◽  
pp. 117-124
Author(s):  
M. Wang ◽  
Y. Ye ◽  
M. Sun ◽  
X. Tan ◽  
L. Li
Keyword(s):  
Template Matching ◽  
Three Dimensional ◽  
Phase Correlation ◽  
Geometric Constraint ◽  
Feature Descriptor ◽  
Automatic Registration ◽  
Geometric Correction ◽  
Registration Accuracy ◽  
Sar Images ◽  
Registration Method

Abstract. Automatic registration of optical and synthetic aperture radar (SAR) images is a challenging task due to significant geometric deformation and radiometric differences between two images. To address this issue, this paper proposes an automatic registration method for optical and SAR images based on spatial geometric constraint and structure features. Firstly, the Harris detector with a block strategy is used to extract evenly distributed feature points in the images. Subsequently, a local geometric correction is performed by using the Rational Function Model, which eliminates the rotation and scale differences between optical and SAR images. Secondly, orientated gradient information of images is used to construct a geometric structural feature descriptor. Then, the feature descriptor is transformed into the frequency domain, and the three-dimensional (3-D) phase correlation is used as the similarity metric to achieve correspondences by employing a template matching scheme. Finally, mismatches are eliminated based on spatial geometric constraint relationship between images, followed by a process of geometric correction to achieve the image registration. Experimental results with multiple high-resolution optical and SAR images show that the proposed method can achieve reliable registration accuracy, and outperforms the state of the art methods.

scholarly journals Technologies for environmental monitoring of the city

2021 ◽  
Author(s):  
O. M. Bahatska ◽  
◽  
N. A. Pasichnyk ◽  
O. O. Opryshko ◽  
◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Agricultural Practices ◽  
Optical Range ◽  
Plant Identification ◽  
Direct Sunlight ◽  
Foreign Objects ◽  
Optical Images ◽  
Optical Satellite Images ◽  
Color Components

IoT technologies in the Big Data concept can radically change approaches in agricultural practices, but it is necessary to work out methods of processing and interpreting information that can be effective in crop practice. Since the dimensions of plants are too small for satellite imagery, the development of technologies can be done on trees whose dimensions are sufficient for their identification in satellite imagery. The purpose of the work is to identify and assess the condition of plantations, in particular trees, with the determination of their positioning on satellite images of megacities. Digital photographs created by optical and infrared lenses of the Obolonskyi district of Kyiv were used for the research. It was found that in the optical range for objects under direct sunlight, plant identification is possible, while shaded areas are identified with significant errors. When using the index for IR shooting IRtree = C1 - C2 + 100 it was possible to identify individual ranges that belong to the crown of trees and grass in direct sunlight and to some extent in the shade, which could not be achieved with the index for optical range GBtree = G - B + 100. Monochrome infrared and optical images were not suitable for plant identification, because when objects were in the shadow of buildings, the ranges of intensity of the color components of plants were superimposed on the ranges of foreign objects. For infrared and optical satellite images, spectral indices have been proposed that take into account several color components to assess the condition of plantations. For tree crowns under direct sunlight, approximately the same results were obtained for the proposed indices. However, the indices proposed for infrared photography are more selective, as they were able to identify separately the crowns of trees and plants on lawns, both in direct sunlight and in the shade of buildings.

scholarly journals Three-dimensional motion analysis of the patellar component in total knee arthroplasty by the image matching method using image correlations

2014 ◽  
Vol 32 (5) ◽  
pp. 619-626 ◽  
Author(s):  
Masami Ishimaru ◽  
Yoshitaka Shiraishi ◽  
Satoru Ikebe ◽  
Hidehiko Higaki ◽  
Kazunori Hino ◽  
...  
Keyword(s):  
Total Knee Arthroplasty ◽  
Motion Analysis ◽  
Knee Arthroplasty ◽  
Image Matching ◽  
Three Dimensional ◽  
Matching Method ◽  
Patellar Component ◽  
Total Knee

scholarly journals A Subpixel Matching Method for Stereovision of Narrow Baseline Remotely Sensed Imagery

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Ning Ma ◽  
Peng-fei Sun ◽  
Yu-bo Men ◽  
Chao-guang Men ◽  
Xiang Li
Keyword(s):  
Image Matching ◽  
Remotely Sensed ◽  
Phase Correlation ◽  
Disparity Estimation ◽  
Matching Method ◽  
Remotely Sensed Imagery ◽  
Novel Variant ◽  
Remotely Sensed Images ◽  
Relationship Of ◽  
The Relationship

In this paper, an accurate and efficient image matching method based on phase correlation is proposed to estimate disparity with subpixel precision, which is used for the stereovision of narrow baseline remotely sensed images. The multistep strategy is adopted in our technical frame; thus the disparity estimation is divided into two steps: integer-pixel prematching and subpixel matching. Firstly, integer-pixel disparity is estimated by employing a cross-based local matching method. Then the relationship of corresponding points is established under the guidance of integer-pixel disparity. The subimages are extracted through selecting the corresponding points as the center. Finally, the subpixel disparity is obtained by matching the subimages utilizing a novel variant of phase correlation approach. The experiment results show that the proposed method can match different kinds of large-sized narrow baseline remotely sensed images and estimate disparity with subpixel precision automatically.

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